MétaCan
Menu
← Retour à la cohorte
Enregistrement W2772174641 · doi:10.1242/jeb.147678

Flying high, no training required

2017· article· en· W2772174641 sur OpenAlexaff
Oana Birceanu

Notice bibliographique

RevueJournal of Experimental Biology · 2017
Typearticle
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueBat Biology and Ecology Studies
Établissements canadiensWilfrid Laurier University
Organismes subventionnairesnon disponible
Mots-clésAltitude (triangle)Effects of high altitude on humansEcologyGeographyZoologyFisheryBiologyMeteorology

Résumé

récupéré en direct d'OpenAlex

Whether it is for the Olympics, pearl diving or climbing Mount Everest, humans have to train intensively to prepare their bodies for extreme challenges. However, it is not entirely clear whether other members of the animal kingdom also go through bouts of training prior to extreme events. Bar-headed geese (Anser indicus) have a reputation for performing one of the most extreme feats at altitude. Migrating across the Himalayas in as little as 7 h, the birds have to sustain one of the most metabolically costly forms of locomotion – flight – in high-altitude conditions, where oxygen is scarce. What is more, these birds fly straight from sea level to altitudes of more than 4500 m in a matter of hours, unlike humans, which need days to gradually habituate to high-altitude conditions. Working with the geese as part of a multinational collaborative project, Lucy Hawkes from the Centre for Ecology and Conservation at the University of Exeter, UK, and her colleagues set out to determine whether these birds ‘train’ for their annual high-altitude migration.Travelling to Terkhiin Tsagaan lake in Mongolia – where the bar-headed geese moult and are unable to fly, during a 2–3 week period prior to migration – Hawkes and colleagues captured small groups of the geese by herding them with inflatable kayaks into shoreline nets. The team then surgically implanted data loggers in the abdomens of the birds to record their heart rate, acceleration and internal temperature and pressure. They then released the animals and recaptured them a year later, when the geese returned to the lake after their migration in order to remove the loggers and download the data.To determine whether the birds trained for their migration, the authors looked at the activity that the animals undertook and the variation in their heart rate during the pre-migratory period. To estimate whether the geese exercised more prior to migration, the authors measured the acceleration produced by the animals as they moved around, which can clearly distinguish flapping from walking and resting. They found that the total amount of activity did not increase prior to migration, nor did the frequency of strenuous flapping on the ground – short, intense ground-based social displays which may also double as strength-training. If the geese were becoming physically fitter, the team also reckoned that their minimum, overnight heart rate would get lower, and their maximum heart rate during flight should get higher, but they found no evidence of either. Compiling the observations, Hawkes and her co-authors could not find evidence that bar-headed geese engage in any type of flight training activity prior to migration.Hawkes and her colleagues also suggest that perhaps bar-headed geese use the rest periods during migration to allow their muscles to recover and rebuild, which may increase their fitness over the course of the migration and could explain the lack of training prior to their departure. In addition, the authors suggest that the cardiac and respiratory systems of the bar-headed geese play an important role in ensuring their impressive performances at altitude, as they are adapted to take up as much oxygen as possible from the environment and quickly deliver it to the organs where it is needed. It appears that a combination of adaptations allows the bar-headed geese to fly high, no training required.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,004
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,260
Score d'incertitude au seuil0,868

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,004
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0020,000
Communication savante0,0010,001
Science ouverte0,0010,002
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,2600,089

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,081
Tête enseignante GPT0,312
Écart entre enseignants0,231 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2017
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueJournal of Experimental Biology→Même sujetBat Biology and Ecology Studies→Travaux en français237 207→